Description
This pretrained pipeline maps entities (Drug Substances) with their corresponding UMLS CUI codes. You’ll just feed your text and it will return the corresponding UMLS codes.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("umls_drug_substance_resolver_pipeline", "en", "clinical/models")
result = pipeline.annotate("The patient was given metformin, lenvatinib and Magnesium hydroxide 100mg/1ml")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = PretrainedPipeline("umls_drug_substance_resolver_pipeline", "en", "clinical/models")
val result = pipeline.annotate("The patient was given metformin, lenvatinib and Magnesium hydroxide 100mg/1ml")
Results
+-----------------------------+---------+---------+
|chunk |ner_label|umls_code|
+-----------------------------+---------+---------+
|metformin |DRUG |C0025598 |
|lenvatinib |DRUG |C2986924 |
|Magnesium hydroxide 100mg/1ml|DRUG |C1134402 |
+-----------------------------+---------+---------+
Model Information
| Model Name: | umls_drug_substance_resolver_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 5.1 GB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperFilterer
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- ResolverMerger